Abstract
The intersection of symbolic and sub-symbolic Artificial Intelligence (AI) presents a fertile ground for innovations that combine the interpretability of the former with the learning capabilities of the latter. This paper introduces Fuzzy Cognitive Maps (FCMs) as a hybrid model that encapsulates the strengths of both paradigms, proposing them as a viable solution to the challenges of explainability and interpretability in AI systems. FCMs have emerged as a compelling framework for representing causal knowledge and facilitating decision-making processes intuitively and justifiably. FCMs can handle the inherent uncertainty and vagueness seen in real-world scenarios, thus enabling a more natural and flexible approach to problem-solving. This intrinsic flexibility, combined with the capacity for learning and adaptation derived from sub-symbolic AI, positions FCMs as an ideal candidate for applications demanding high degrees of explainability and interpretability.